Artificial intelligence is everywhere. In fact, each reader of this article could have multiple AI apps operating on the very device displaying this piece. The image at the top of this article is also generated by AI.
Despite this, many mechanisms governing AI behaviour remain poorly understood, even to top AI experts. This leads to an AI race built upon costly scaling, both environmentally and financially, that is also dangerously unreliable.
Progress therefore depends not on escalating this race, but on understanding the principles underpinning AI. Mathematics lies at the heart of AI and investment in these mathematical foundations is the critical key to becoming a true global AI leader.
How AI shapes daily life
AI has rapidly become part of everyday life, not only in talking home devices and fun social media generation, but also in ways so seamless that many people don’t even notice its presence.
It provides the recommendations we see when browsing online and quietly optimizes everything from transit routes to home energy use.
Critical services rely on AI because it’s used in medical diagnosis, banking fraud detection, drug discovery, criminal sentencing, governmental services and health predictions, all areas where inaccurate outputs may have devastating consequences.
Problems, issues
Despite AI’s widespread use, serious and widely documented issues continue to showcase concerns around fairness, reliability and sustainability. Biases embedded in data and models can propagate discriminatory outcomes, from facial detection methods that perform well only on light skin tones to predictive tools that systematically disadvantage underrepresented groups.
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These failures continue to be reported and range from racist outputs of ChatGPT and other chatbots to imaging tools that misidentify Barack Obama as white and biased criminal sentencing algorithms.
At the same time, the environmental and financial costs of deploying large-scale AI systems are growing at an extremely rapid pace.
If this trajectory continues, it will not only prove environmentally unsustainable, it will also concentrate access to these powerful AI tools to a few wealthy and influential entities with access to vast capital and massive infrastructure.
THE CANADIAN PRESS/Darryl Dyck
Why mathematics?
To address issues with a system, whether it’s fixing a car or ensuring reliability in an AI system, it’s crucial to understand how it works. A mechanic cannot fix or even diagnose why a car isn’t operating correctly without understanding how the engine works.
The “engine” for AI is mathematics. In the 1950s, scientists used ideas from logic and probability to teach com